print 'Remaining receivers are: ', interf_map
print 'Interference indicator sets are'
print Interferers
#### for quick test, should be replaced with symbol/message data structures
K = len(interf_map)
W = np.matrix(np.zeros((K, 1)))
A = np.matrix(np.zeros((K, K)))
I = (Interferers - 1) * -1
I[np.diag_indices_from(I)] = 0
for index, node in enumerate(interf_map):
W[index, 0] = sym_vec[node].val
for jindex, i in enumerate(I[index, :]):
if i == 1:
A[index, jindex] = sym_vec[interf_map[jindex]].val
## nuclear norm approx
num_transmissions = 2 * K # int(K/2) + 1
print
print 'num transmissions is ', num_transmissions
V, U = nuclear_alignment(Interferers, num_transmissions, .1, 1)
print
print 'U.T*V is:'
print U.T * V
print
for k in range(K):
W_dec = ((U.T * V)[k, :] * W -
(U.T * V)[k, :] * A[k, :].T) / (U.T * V)[k, k]
print 'Message %d truth: %d decoded: %.2f' % (k, W[k], W_dec)
print 'Interference indicator sets are' print Interferers #### for quick test, should be replaced with symbol/message data structures K = len(interf_map) W = np.matrix(np.zeros((K,1))) A = np.matrix(np.zeros((K, K))) I = (Interferers - 1)*-1 I[np.diag_indices_from(I)] = 0 for index,node in enumerate(interf_map): W[index,0] = sym_vec[node].val for jindex,i in enumerate(I[index,:]): if i == 1: A[index,jindex] = sym_vec[interf_map[jindex]].val ## nuclear norm approx num_transmissions = 2*K # int(K/2) + 1 print print 'num transmissions is ', num_transmissions V, U = nuclear_alignment(Interferers, num_transmissions, .1, 1) print print 'U.T*V is:' print U.T*V print for k in range(K): W_dec = ((U.T*V)[k,:]*W - (U.T*V)[k,:]*A[k,:].T)/(U.T*V)[k,k] print 'Message %d truth: %d decoded: %.2f' % (k, W[k], W_dec)
# U: [m,K] initialization decoding matrix
# epsilon: threshold for min. eigenvalue
# iter: number of iterations
#def nuclear_IA_K_user(I, U, epsilon=.1, iter=5):
# obtain problem parameters
#I = np.array([ [0,1,1], [1,0,0], [1,0,0] ])
#I = np.array([ [0,1,0,1], [0,0,1,0], [1,1,0,0], [0,0,1,0] ])
I = np.zeros((5, 5))
epsilon = .1
iter = 1
K = np.size(I, 0)
m = 1
V, U = nuclear_alignment(I, m, epsilon, iter, quiet=False)
print
print U.T * V
print
print I
W = np.random.randint(1, 255, [K, 1])
A = np.matrix(np.zeros((K, K)))
for k in range(K):
for l in range(K):
if k != l and I[k, l] != 1:
A[k, l] = W[l]
print
# U: [m,K] initialization decoding matrix # epsilon: threshold for min. eigenvalue # iter: number of iterations #def nuclear_IA_K_user(I, U, epsilon=.1, iter=5): # obtain problem parameters #I = np.array([ [0,1,1], [1,0,0], [1,0,0] ]) #I = np.array([ [0,1,0,1], [0,0,1,0], [1,1,0,0], [0,0,1,0] ]) I = np.zeros((5,5)) epsilon = .1 iter = 1 K = np.size(I,0) m = 1 V,U = nuclear_alignment(I, m, epsilon, iter, quiet=False) print print U.T*V print print I W = np.random.randint(1,255,[K,1]) A = np.matrix(np.zeros((K,K))) for k in range(K): for l in range(K): if k != l and I[k,l] != 1: A[k,l] = W[l]
